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Paketbeschreibung


Paketnamelibranlip-dev
Beschreibunggenerates random variates with multivariate Lipschitz density
Archiv/RepositoryOffizielles Ubuntu Archiv lucid (universe)
Version1.0-4.1
Sektionuniverse/libdevel
Prioritätoptional
Installierte Größe84 Byte
Hängt ab vonlibc6 (>= 2.7-1), libgcc1, libranlip1c2 (= 1.0-4.1), libstdc++6 (>= 4.1.1-21), libtnt-dev
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PaketbetreuerUbuntu MOTU Developers
Quellelibranlip
Paketgröße15330 Byte
Prüfsumme MD554d8d264c587f15b04eedf7de4d7cb5c
Prüfsumme SHA13a8d8e248640f5248813eeecd8a3b94bf03fcd60
Prüfsumme SHA2568ba4d040552e1980656d9d0aacdc0bafd34f770b1a15e1c37b2db592d1c59414
Link zum Herunterladenlibranlip-dev_1.0-4.1_i386.deb
Ausführliche BeschreibungRanLip generates random variates with an arbitrary multivariate Lipschitz density. . While generation of random numbers from a variety of distributions is implemented in many packages (like GSL library http://www.gnu.org/software/gsl/ and UNURAN library http://statistik.wu-wien.ac.at/unuran/), generation of random variate with an arbitrary distribution, especially in the multivariate case, is a very challenging task. RanLip is a method of generation of random variates with arbitrary Lipschitz-continuous densities, which works in the univariate and multivariate cases, if the dimension is not very large (say 3-10 variables). . Lipschitz condition implies that the rate of change of the function (in this case, probability density p(x)) is bounded: . |p(x)-p(y)|=p(x), using a number of values of p(x) at some points. The more values we use, the better is the hat function. The method of acceptance/rejection then works as follows: generatea random variate X with density h(x); generate an independent uniform on (0,1) random number Z; if p(X)<=Z h(X), then return X, otherwise repeat all the above steps. . RanLip constructs a piecewise constant hat function of the required density p(x) by subdividing the domain of p (an n-dimensional rectangle) into many smaller rectangles, and computes the upper bound on p(x) within each of these rectangles, and uses this upper bound as the value of the hat function.

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